Aiming at the problems such as the uncertainty of distributed power output and the low efficiency of operation in the coupled system scheduling of distribution network and microgrid

an optimized scheduling model of Branch-bar operation with chance constraint based on the integration of deep learning method for distribution network and microgrid interconnection system is proposed. The uncertainty probability set of renewable energy and load of microgrid is constructed based on probabilistic output support vector machine

Bayesian neural network and deep belief network. The D-S evidence theory information integration framework is established

the evidence correction method based on Kappa coefficient and accuracy weight is proposed

the evidence is revised from the output and load power of renewable energy

and then

the uncertainty probability set with higher precision is obtained

and the probability distribution fuzzy set of source load power is obtained. The two-stage rolling scheduling optimization model of multi-microgrid is established

namely

the first-stage pre-scheduling model and the second-stage real-time regulation model. In the first stage

the energy pre-allocation is carried out to achieve the optimal global operation economy of multi-microgrid region. The second stage is the real-time operation control stage. Considering the uncertainty of the real-time output of new energy in the microgrid

the proposed two-stage robust economic dispatching model adopts column-and-constraint generation(CCG) and alternating direction multiplier method(ADMM) combines the column and constraint generation algorithm and joint target cascade analysis algorithm for distributed solution. The simulation results show that the safe and reliable operation of the distribution-microgrid market can be effectively improved under the uncertainty of the source load prediction

and the new energy consumption rate and economic benefits of the interconnected system can be improved.